1. Technical Field of the Invention
This invention relates to three-dimensional (3D) biometric data processing and in particular to systems and methods for processing 3D biometric data for feature detection, classification and recognition.
2. Description of Related Art
Biometrics is the science of measuring and analyzing biological data. In law enforcement and security fields, biometrics is used to measure and analyze human features, such as fingerprints, facial patterns, palm prints, retinas, etc. Currently, binary or two-dimensional fingerprint images are the most relied upon biometric measurement for verifying a person's identity and for linking persons to a criminal history and for background checks. Criminal justice agencies rely on binary fingerprint images for positive identification to latent prints collected as evidence at crime scenes and in processing persons through the criminal justice system.
The National Institute of Science and Technology (NIST) and the American National Standards Institute (ANSI) supports the ANSI/NIST-ITL 1-2000 Data Format for the Interchange of Fingerprint, Facial, & Scar Mark & Tattoo (SMT) Information. This standard defines the content, format, and units of measurement for the exchange of biometric image information, such as fingerprint, palm print, facial/mug shot, and scar, mark, & tattoo (SMT) image information that may be used in the identification process of a subject. The information consists of a variety of mandatory and optional items, including scanning parameters, related descriptive and record data, digitized fingerprint information, and compressed or uncompressed images. This information is intended for interchange among criminal justice administrations or organizations that rely on automated fingerprint and palm print identification systems or use facial/mug shot or SMT data for identification purposes. Other organizations have different standards as well for the content, format or units of measurement for biometric information. The fingerprint and palm-print images meeting specified NIST and ANSI standards allow for matching of print images in large databases of existing fingerprint and palm-print based records. For example, the FBI maintains an Interstate Identification Index System for fingerprints and palm prints. Currently, the biometric image information required by NIST and ANSI standards for fingerprints and palm prints includes only binary biometric images or two-dimensional coordinates of fingerprint features.
In addition, the most common methods of finger print and palm print acquisition collect two-dimensional biometric images. One method is a rolled ink print technique wherein a trained technician manipulates a person's finger or hand on paper to capture an ink print that meets industry standards. Another method is electronic scanning of a finger or palm as it contacts a surface to generate a two-dimensional (2D) binary image.
Systems are currently being developed for non-contact means of acquiring fingerprints using 3D imaging techniques. Recent advancements in the fields of computing hardware and software have made capturing 3D biometric data a more practical solution. For example, one method of capturing 3D biometric image data is described in PCT Application No. WO2007/050776, entitled System and Method for 3D Imaging using Structured Light Illumination, which is incorporated by reference herein.
For backward compatibility with the existing fingerprint databases, acquired 3D biometric image data is converted into a gray scale 2D image. In addition, the 2D gray scale image may be further converted into a 2D binary image. For example, an l(x,y) value is generated to represent the gray-level of a point of the fingerprint in two dimensions. A binarization of the gray scale image is then performed by assigning gray levels close to 0 to surface valleys and dark pixels with high values of gray to ridges. The ridges and valleys of the 2D binary image are then processed to detect features of the fingerprint. The detected features of the fingerprint are described with a location having two-dimensional coordinates. The detected features then are used to perform validation, classification and recognition tasks.